Spatial Analysis of Environmental Hazards

Spatial Analysis of Environmental Hazards

Sycamore Failure Hazard Risk modeling in urban green space

Author
Ali Jahani
10.18869/acadpub.jsaeh.3.4.35
Abstract
Trees in urban areas have survived in a wide variety of conditions and constrains, whether developing in natural or manmade habitats. Due to environmental constrains and stresses, urban trees rarely achieve their biological potentials. Indeed, some of trees, in small groups, could excel in terms of age, biomass structure and dimensions in urban areas. In definition, tree hazard includes entirely dead or dying trees, dead parts of harmed live trees, or extremely unstable or unsteady live trees, which could be in result of structural defects and disorders or other factors that have the high risk to threaten the safety of people or property in the event of a failure especially in urban green spaces. Although the pruning or other rehabilitation and mitigation program of trees is known as the one of the principal domains of green space management, it is still includes shortcomings in terms of models and methodologies to classify or prioritize hazardous trees which need to be treated timely. The main objectives of this study were to: (1) model old Sycamore failure hazard in urban green spaces to elucidate the general and defects tree factors affecting on failure hazard; (2) prioritize the impacts of model inputs (general and defects tree factors) on tree failure hazard using model sensitivity analysis and (3) determining the trend model output changes in respond to model variables changes.

The following types of data (target trees characteristics) were solicited for each target tree: (1) General features: Tree Height (TH), trunk Diameter at Breast Height (DBH), Butt Diameter (BD) at ground surface and Vertical Length of Crown (VLC) were calculated from measured girth. Crown Spread (CS) was measured as the average of two diameters of projected drip line of the tree canopy.

(2) Tree defects: Detailed evaluation of individual trees was made according to 6 key physical defects, namely Internal Decay (ID) in percent, Length of Cracks (LC) in m, Crown Defoliation (CD) in percent, and Degree of Leaning (DL).

(3) Sycamore failure hazard classification: Sycamore Failure Hazard Risk (SFHR) classification was the probability that an entire tree, or part of it, will break and fall within the first or second year after study. Considering results of tree regular monitoring after two years, the following classes of tree failure hazard were determined. 1. Extremely Hazardous: Tree failure in the first year. 2. Semi-Hazardous: Tree failure in the second year.

ANN has been recently developed for data mining, pattern recognition, quality control, and has gained wide popularity in modeling of many processes in environmental sciences and engineering. ANN learns by examples and it can combine a large number of variables. In this study, an ANN is considered as a computer program capable of learning from samples, without requiring a prior knowledge of the relationships between parameters. To objectively evaluate the performance of the network, two different statistical indicators were used. These indicators are Mean-Squared Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2).

In this study, the year of Sycamore failure in urban ecosystems is evaluated using tree variables and artificial neural network to determine the most effective tree variables in SFHR in urban green space. Various MLFNs were designed and trained as one and two layers to find an optimal model prediction for the SFHR and variables. Training procedure of the networks was as follows: different hidden layer neurons and arrangements were adapted to select the best production results. Altogether, many configurations with different number of hidden layers (varied between one and two), different number of neurons for each of the hidden layers, and different inter-unit connection mechanisms were designed and tested.

In this research, 200 trees were totally selected, then general and defects tree variables were recorded in urban green space. Considering the aim of study, which is discovering the relation between general and defects tree variables with SFHR class for modeling, the year of tree failure, was recorded.

In the structure of artificial neural network, general and defects tree variables were tagged as inputs of artificial neural network and SFHR class was tagged as output layer. Considering trained networks (the structure of optimum artificial neural network has been summarized in Table1), Multilayer Perceptron network with one hidden layer and 4 neurons in layer created the best function of topology optimization (Table2) with higher coefficient of determination which equals 0.87 for class 1 and 0.9 for class 2. Sensitivity analysis respectively prioritizes Crown Spread (CS), Vertical Length of Crown (VLC), Degree of Leaning (DL) and Butt Diameter (BD), which effect on SFHR in class1 (Fig1) and class 2 (Fig2).

The determined procedure of SFHR changes with CS changes in the region declares SFHR increase nonlinearly with an increase in CS. The determined procedure of SFHR changes with VLC changes o declares that SFHR increase nonlinearly with an increase in VLC of tree. The determined procedure of SFHR changes with DL changes in the region declares SFHR increase nonlinearly with an increase in DL. The determined procedure of SFHR changes with BD changes o declares that SFHR increase nonlinearly with an increase in BD of tree.

Nowadays, artificial neural network modeling in natural environments has been applied successfully in many researches such as water resources management, forest sciences and environment assessment. The results of research declared that designed neural network shows high capability in SFHR modeling which is applicable in green space management of studied area. Sensitivity analysis identified the most effective variables which are influencing SFHR. So, to identify hazardous trees in study area, we should pay attention to the CS of Sycamore trees as the variable with high priority in determination of SFHR. We believe that, in hazardous trees management in urban green spaces, we should pay attention to some modifiable factors of tree, which are CS and VLC, by timely tree pruning. We suggest urban green space manager to run SFHR model, for tree stability assessment, before decision making on hazardous trees.
Keywords
hazardous tree
SFHR
Artificial neural network
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